Conventional art methods of predicting a yield of a semiconductor manufacturing process generally involve testing a small number of the first wafers produced and mapping their yield to subsequent production. For example, 40% of finished die on the first wafers met a set of specifications, and thus 40% of all die on all wafers are predicted to meet those specifications.
Unfortunately, such conventional methods generally do not account for process target drift, and/or changes in process variation. In addition, such first wafers often are not representative of an entire manufacturing population. Thus, tested parameters must be forced into an assumed distribution, e.g., a normal distribution, to predict manufacturing yield. Such an assumed distribution, based on limited sampling, may not reflect a distribution of a larger manufacturing population.